Earticle

초록

영어

For multi-level semantic structure, the asymmetry of similarity between semantic concepts, as well as the different correlation of semantics between the children nodes and father node, this dissertation proposed a novel similarity calculation method of image semantic based on the probabilistic weighting. This method combines the image feature mapping the visual characteristics of the underlying semantic with the domain ontology description to build a tree-like hierarchical semantic model. According to what the posterior probability and conditional probability were gained by Bayesian network learning, and further for those semantic similarity who are based on semantic distance took the weighted processing so as to get the final similarity of image semantic. Moreover, taking the medical image semantics as the experiment object in weighted method can improve the retrieval performance compared with the non-weighted similarity calculation method.

목차

Abstract
 1. Introduction
 2. Multi-level Concept of Semantic Similarity Measures
  2.1. Tree-based Semantic Similarity Measure
  2.2. Multi-level Semantic Description Model
  2.3. Probability-weighted Semantic Similarity Calculation
  2.4 Application Example
 3. Experimental Results
  3.1. Experimental Comparison of Weighted Semantic Similarity
  3.2. Comparative Experimental Results of Image Semantic Retrieval
 4. Conclusion
 Acknowledgements
 References

키워드

저자

• Qian Wang [ School of Computer Science ]
• Chunli Zhang [ School of Electrical and Electronic Engineering, Harbin University of Science and Technology Harbin 150080, China ]
• Lixin Song [ School of Electrical and Electronic Engineering, Harbin University of Science and Technology Harbin 150080, China ]

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  International Journal of Multimedia and Ubiquitous Engineering

• 간기

  월간

• pISSN

  1975-0080

• 수록기간

  2008~2016

• 등재여부

  SCOPUS

• 십진분류

  KDC 505 DDC 605